Learning semantically interpretable image representations has many benefits, such as better generalization [1]}, robustness to noise [2]}, and increased transfer performance [3]}. Disentanglement can add interpretability if each representation dimension maps to one factor of variation present in the images while being relatively invariant to changes in the other factors [4]}; this is the definition of disentanglement that we adopt in this work.
